Internal

PiperOrigin-RevId: 713813816

checkpoint/orbax/checkpoint/experimental/emergency/multihost.py

@@ -93,7 +93,7 @@ def process_index_from_device_id(device_id: int) -> int:
   # CPU workload.
   else:
     # This is based on how CPU device ids are assigned.
-    # See third_party/tensorflow/compiler/xla/pjrt/cpu/cpu_topology.h.
+    # See tensorflow/compiler/xla/pjrt/cpu/cpu_topology.h.
     return device_id // (1 << 17) // jax.local_device_count()
 
 

experimental/model/README.md

@@ -0,0 +1,134 @@
+# Orbax Model
+
+Orbax Model (OBM) is the next-generation framework-agnostic export and serialization library for [JAX](https://jax.readthedocs.io/en/latest/) model export (and later, for PyTorch). It can be used as an alternative to [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model). 
+
+---
+
+## Table of Contents
+
+1. [Introduction](#introduction)
+2. [Features](#features)
+3. [Installation](#installation)
+4. [Usage](#usage)
+
+---
+
+## Introduction
+
+Currently there are many ways of ML models export for production inference, causing fragmentation issues and confusion to users.
+
+Orbax Model (OBM) aims to provide a framework-agnostic export library by elevating [StableHLO](https://openxla.org/stablehlo) to be a first-class element of the model format. It can be used as a drop-in replacement for TensorFlow SavedModel, and later for PyTorch.
+
+The Orbax Model (OBM) project actually consists of three separate codebases: OBM Core, JAX2OBM and TF2OBM. Both JAX2OBM and TF2OBM depend on OBM. TF2OBM also depends on JAX2OBM in order to provide some utility functions that involve both TF and JAX.
+
+- OBM Core: underlying core library
+- JAX2OBM: JAX-specific export library to Orbax Model format
+- TF2OBM: TensorFlow-specific export library to Orbax Model format
+
+![OBM Architecture](./obm.png)  ![obm_legend](./obm_legend.png)
+---
+
+## Features
+
+- Modular export API and protobuf format with a TensorFlow-free version.
+- TF functions are popular for pre/post-processing in Jax models. They continue to work.
+- Orchestration of JAX functions, checkpoints and TF pre/post-processing functions.
+- No conversions or wrappers around JAX functions. StableHLO and XLA sharding metadata are first class citizens.
+- Pytree-like type system to describe function signatures.
+- The model can be loaded back in JAX without the original source code.
+
+---
+
+## Installation
+
+TODO(qidichen): add step by step commands / instructions for installation.
+
+---
+
+## Usage
+
+Convert the JAX function to an OBM function:
+
+```python
+from orbax_model import jax2obm
+from orbax_model import tf2obm
+
+args_spec, kwargs_spec = tf2obm.tf_specs_to_jax(
+    pre_processor.structured_outputs)
+obm_apply_fn = jax2obm.convert(apply_fn, args_spec, kwargs_spec)
+
+```
+
+Convert the TF concrete functions to OBM functions and save to disk:
+
+```python
+obm_pre_processor = tf2obm.tf_concrete_function_name_to_obm_function(
+    "my_tf_pre_processor", pre_processor)
+obm_post_processor = tf2obm.tf_concrete_function_name_to_obm_function(
+    "my_tf_post_processor", post_processor)
+
+
+tf2obm.save_tf_concrete_functions(
+    "path/to/my_orbax_model/tf_stuff/",
+    {"my_tf_pre_processor": pre_processor,
+     "my_tf_post_processor": post_processor},
+    trackable_resources)
+```
+
+Convert the OCP (Orbax Checkpointing) folder name into an OBM value:
+
+```python
+from orbax_model import core as obm
+
+obm_weights = obm.convert_path_to_value(
+    "path/to/my_orbax_model/my_checkpoint/",
+    mime_type="orbax_checkpoint")
+```
+
+Create a simple Python dictionary to contain all the above OBM functions and OBM values:
+
+```python
+obm_dict = {"my_model_fn": obm_apply_fn,
+     "my_weights": obm_weights,
+     "my_pre_processor": obm_pre_processor,
+     "my_post_processor": obm_post_processor}
+```
+
+Create an orchestration to record how those OBM objects interact with each other, using the names in the previous step to refer to those objects:
+
+```python
+import orbax_export as oex
+
+orchestration = oex.orchestration(
+    model_function_name="my_model_fn",
+    weights_name="my_weights",
+    pre_processor_name="my_pre_processor",
+    post_processor_name="my_post_processor")
+```
+
+Save the object dictionary, the orchestration, and (the filename of) the previously generated TF SavedModel onto disk as an Orbax Model:
+
+```python
+obm.save(obm_dict, "path/to/my_orbax_model/", obm.SaveOptions(
+    version=2,
+    supplemental_info={
+        "orchestration": obm.GlobalSupplemental(
+            orchestration, "my_orchestration.pb"),
+        "tensorflow_saved_model": obm.GlobalSupplemental(
+            tf2obm.tf_saved_model_as_obm_supplemental("tf_stuff/"), None)}))
+```
+
+Finally, the generated OBM folder will look like this:
+
+```
+path/to/my_orbax_model/
+  manifest.pb
+  my_orchestration.pb
+  <some StableHLO graphs>
+  <some JAX-specific info>
+  my_checkpoint/
+    <OCP stuff>
+  tf_stuff/
+    saved_model.pb
+    <SavedModel stuff>
+```

model/orbax/model/core/LICENSE

@@ -0,0 +1,202 @@
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model/orbax/model/core/README.md

@@ -0,0 +1,8 @@
+# ML Export Library
+
+A framework-agnostic library for exporting machine learning models.
+
+Currently contains copies of TensorFlow SavedModel proto definitions. Do not use since this is currently experimental.
+
+Other than some integration tests, anything in this folder should not depend on
+any specific ML framework such as TF, JAX or PyTorch.

model/orbax/model/core/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2024 The Orbax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""ML Exported Model API."""
+
+from orbax.experimental.model.core.python import *

model/orbax/model/core/build_defs.bzl

@@ -0,0 +1,17 @@
+"""Build macro for proto fork."""
+
+load("//devtools/build_cleaner/skylark:build_defs.bzl", "register_extension_info")
+load("@rules_python//python:proto.bzl", "py_proto_library")
+load("@rules_proto//proto:defs.bzl", "proto_library")
+load("//tools/build_defs/proto/cpp:cc_proto_library.bzl", "cc_proto_library")
+
+def ml_proto_library(name, srcs = [], deps = []):
+    proto_library(name = name, srcs = srcs, deps = deps)
+    name_prefix = name[:name.index("_proto")]
+    cc_proto_library(name = name_prefix + "_cc_proto", deps = [":" + name])
+    py_proto_library(name = name_prefix + "_py_pb2", deps = [":" + name])
+
+register_extension_info(
+    extension = ml_proto_library,
+    label_regex_for_dep = "{extension_name}",
+)